Question

我在Encog中创建并学习了自动编码器，我尝试将其分成几部分：编码器和解码器部分。不幸的是我无法得到它并且我一直得到奇怪的不正确的数据（比较从一次网络应用到数据和两次数据的结果 - ＆gt; enc - ＆gt; dec）。

我试图用简单的GetWeight和SetWeight来制作它，但结果不正确。在encog文档中找到的解决方案 - 初始化平面网络对我来说并不清楚（我无法让它工作）。

        public static BasicNetwork getEncoder(BasicNetwork net)
        {
            var enc = new BasicNetwork();
            enc.AddLayer(new BasicLayer(null, true, net.GetLayerNeuronCount(0)));
            enc.AddLayer(new BasicLayer(new ActivationSigmoid(), true, net.GetLayerNeuronCount(1)));
            enc.AddLayer(new BasicLayer(new ActivationSigmoid(), false, net.GetLayerNeuronCount(2)));
            enc.Structure.FinalizeStructure ();

            var weights1 = net.Structure.Flat.Weights;
            var weights2 = enc.Structure.Flat.Weights;
            var idx1 = net.Structure.Flat.WeightIndex;
            var idx2 = enc.Structure.Flat.WeightIndex;

            for(var i = 0; i < 1; i++)
            {
                int n = net.GetLayerNeuronCount(i);
                int m = net.GetLayerNeuronCount(i + 1);

                Console.WriteLine("Decoder: {0} - {1}", n, m);

                for(var j = 0; j < n; j++)
                {
                    for(var k = 0; k < m; k++)
                    {
                        weights1 [idx1[i] + j * m + k] = weights2 [idx2[i] + j * m * k];
                    }
                }
            }


            return enc;
        }

AutoEncoder的完全旧的（设置/获取权重）代码：

using System;
using Encog.Engine.Network.Activation;
using Encog.ML.Data;
using Encog.ML.Data.Basic;
using Encog.ML.Train;
using Encog.Neural.Networks;
using Encog.Neural.Networks.Layers;
using Encog.Neural.Networks.Training.Propagation.Resilient;

namespace engine
{
    public class AutoEncoder
    {
        private int k = 0;
        public IMLDataSet trainingSet
        {
            get;
            set;
        }

        public AutoEncoder(int k)
        {
            this.k = k;
        }

        public static BasicNetwork getDecoder(BasicNetwork net)
        {
            var dec = new BasicNetwork();
            dec.AddLayer(new BasicLayer(null, true, net.GetLayerNeuronCount(1)));
            dec.AddLayer(new BasicLayer(new ActivationSigmoid(), true, net.GetLayerNeuronCount(2)));

            dec.Structure.FinalizeStructure();

            for(var i = 1; i < 2; i++)
            {
                int n = net.GetLayerNeuronCount(i);
                int m = net.GetLayerNeuronCount(i + 1);

                Console.WriteLine("Decoder: {0} - {1}", n, m);

                for(var j = 0; j < n; j++)
                {
                    for(var k = 0; k < m; k++)
                    {
                        dec.SetWeight(i - 1, j, k, net.GetWeight(i, j, k));
                    }
                }
            }

            return dec;
        }

        public static BasicNetwork getEncoder(BasicNetwork net)
        {
            var enc = new BasicNetwork();
            enc.AddLayer(new BasicLayer(null, true, net.GetLayerNeuronCount(0)));
            enc.AddLayer(new BasicLayer(new ActivationSigmoid(), true, net.GetLayerNeuronCount(1)));

            enc.Structure.FinalizeStructure();

            for(var i = 0; i < 1; i++)
            {
                int n = net.GetLayerNeuronCount(i);
                int m = net.GetLayerNeuronCount(i + 1);

                Console.WriteLine("Encoder: {0} - {1}", n, m);

                for(var j = 0; j < n; j++)
                {
                    for(var k = 0; k < m; k++)
                    {
                        enc.SetWeight(i, j, k, net.GetWeight(i, j, k));
                    }
                }
            }

            return enc;
        }

        public BasicNetwork learn(double[][] data,
            double eps = 1e-6,
            long trainMaxIter = 10000)
        {
            int n = data.Length;
            int m = data[0].Length;
            double[][] output = new double[n][];
            for(var i = 0; i < n; i++)
            {
                output[i] = new double[m];
                data[i].CopyTo(output[i], 0);
            }

            var network = new BasicNetwork();
            network.AddLayer(new BasicLayer(null, true, m));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, k));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, m));
            network.Structure.FinalizeStructure();
            network.Reset();

            trainingSet = new BasicMLDataSet(data, output);
            IMLTrain train = new ResilientPropagation(network, trainingSet);

            int epoch = 1;
            do
            {
                train.Iteration();
                Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
                epoch++;
            } while(train.Error > eps && epoch < trainMaxIter);

            train.FinishTraining();

            return network;
        }
    }
}

如何才能正确地从编码器的自动编码器中剥离两个第一层，从一个解码器中另外两个最后一层？

Answer 1

如果您需要直接访问权重，最好的方法是使用BasicNetwork.GetWeight（）。这是一个示例，显示如何使用GetWeight获取神经网络中的所有权重。它来自单元测试，证明GetWeight确实有效，它使用BasicNetwork.Compute计算简单神经网络的输出，也可以通过对加权输入求和并应用TanH来手动计算。两者都会产生相同的输出。

如果您想直接访问权重数组，也可以在此处获取更多信息：http://www.heatonresearch.com/wiki/Weight

        var network = new BasicNetwork();
        network.AddLayer(new BasicLayer(null, true, 2));
        network.AddLayer(new BasicLayer(new ActivationTANH(), true, 2));
        network.AddLayer(new BasicLayer(new ActivationTANH(), false, 1));
        network.Structure.FinalizeStructure();
        network.Reset(100);

        BasicMLData input = new BasicMLData(2);
        input[0] = 0.1;
        input[1] = 0.2;

        Console.WriteLine("Using network: " + network.Compute(input));

        // now manually
        double sum1 = (input[0]*network.GetWeight(0, 0, 0))
                      + (input[1]*network.GetWeight(0, 1, 0))
                      + (1.0*network.GetWeight(0,2,0));

        double sum2 = (input[0]*network.GetWeight(0, 0, 1))
                      + (input[1]*network.GetWeight(0, 1, 1))
                      + (1.0*network.GetWeight(0,2,1));

        double hidden1 = Math.Tanh(sum1);
        double hidden2 = Math.Tanh(sum2);

        double sum3 = (hidden1 * network.GetWeight(1, 0, 0))
                      + (hidden2 * network.GetWeight(1, 1, 0))
                      + (1.0 * network.GetWeight(1, 2, 0));

        double output = Math.Tanh(sum3);

        Console.WriteLine("Using manual: " + network.Compute(input));

编码器/解码器从encog中的autoencoder中翻录出来

1 个答案: